Course Catalog

Study Program SoSe 2022

Digitale Medien, B.Sc.

3. Studienjahr

B-MI-9

Auch Module aus B-MI-8 hier wählbar.
Hinweis: Studierende, die das Software-Projekt machen möchten, müssen bitte alle drei angebotenen Veranstaltungen hierzu belegen: 03-BA-901.01a (SWP1), 03-BA-901.01b (Datenbankgrundlagen) und 03-BA-901.01c (SWP Praktikum).
Bei Vorliegen der jeweiligen inhaltlichen Voraussetzungen auch: M-MI/ M-MI-d des Master
Course numberTitle of eventLecturer
03-IBAP-CS (03-BB-711.01)Cognitive Systems (in English)
Grundlagen der Informationsverarbeitung in natürlichen und künstlichen Systemen

Lecture (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Tue. 08:00 - 10:00 GW2 B1410 Vorlesung Präsenz
weekly (starts in week: 1) Wed. 08:00 - 10:00 CART Rotunde - 0.67 Übung Präsenz
weekly (starts in week: 1) Wed. 10:00 - 12:00 CART Rotunde - 0.67 Übung Präsenz
Thomas Dieter Barkowsky
03-IBAP-ML (03-BB-710.10)Grundlagen des Maschinellen Lernens (in English)
Fundamentals of Machine Learning

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Mon. 14:00 - 16:00 MZH 3150 Übung Präsenz
weekly (starts in week: 1) Wed. 10:00 - 12:00 MZH 1380/1400 MZH 6200 Vorlesung Präsenz
weekly (starts in week: 1) Wed. 14:00 - 16:00 MZH 1100 Übung Präsenz

Additional dates:
Wed. 27.07.22 10:00 - 14:00 MZH 1380/1400
Wed. 27.07.22 10:00 - 14:00 MZH 1470

Schwerpunkt: AI

Tanja Schultz
Felix Putze
Mazen Salous
Darius Ivucic
Gabriel Ivucic

B-MA-2

Auch Module aus den Bereichen B-MI-8 und B-MI-9 sind hier wählbar.
Für Lehrveranstaltungen dieses Moduls der Hochschule für Künste bitte das dortige Lehrveranstaltungsverzeichnis ansehen: http://www.hfk-bremen.de/t/digitale-medien
Course numberTitle of eventLecturer
03-IBVA-DS (03-BE-802.98a)Data Science (in English)
Applied Machine Learning

Kurs (Teaching)
ECTS: 6

Dates:
weekly (starts in week: 1) Mon. 16:00 - 18:00 Online Kurs online

From medical decision support systems to automatic language translation, from sorting and prioritizing news on social networks to autonomous cars: Machine learning is woven into the fabric of daily life. Applying machine learning, data science aims to extract knowledge or insights from data.

The class will provide an introduction to data science and applied machine learning. For this, the programming language Python will be used (and taught). You will learn about the difference between supervised and unsupervised machine learning, and four machine learning tasks:
• Classification (e.g. k-NN, Decision Trees, Support Vector Machines)
• Regression (Linear Regression, Logistic Regression)
• Clustering (k-means)
• Dimensionality Reduction (PCA, t-SNE)
We will explore natural language processing for text mining and computer vision. Exploratory data analysis and evaluation, as an integral part of data science, will also be taught.

This class is taught remotely. Every week, the lecturer will upload new material to this website. To succeed in this course, you have to watch the videos, do the exercises and applications, and work on your own project. Remember that these videos are not full-fledged lectures, they are a starting point for your own learning. Use material like the coursebook to learn more about the topics as we progress in the course.

This is an online course, not a lecture that was filmed and put online. The course format was adapted to suit both the needs of the medium and the material.

We will meet regularly, but most of the input will be provided as videos. This allows you to rewatch videos, watch them at different speeds, and discuss the videos with each other.

Prof. Dr. Hendrik Heuer
Dr. Juliane Jarke

Graduiertenseminare

Course numberTitle of eventLecturer
03-IGRAD-CoSy (03-05-H-711.91)Graduiertenseminar Cognitive Systems (in English)

Seminar (Teaching)

Dates:
fortnightly (starts in week: 16) Wed. 14:00 - 17:00 Graduiertenseminar
Thomas Dieter Barkowsky

Informatica Feminale

Course numberTitle of eventLecturer
META-2022-ALL-IF25. internationale Informatica Feminale (in English)
Informatica Feminale - Summer University for Women in Computing
Sommeruniversität für Frauen in der Informatik /Summer University for Women in Computing

Blockveranstaltung (Teaching)
ECTS: 1-3 (je Kurs/for every course)

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2022 sowie im Wintersemester 2022/23 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2022 as well as in winter semester 2022/23. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veronika Oechtering
Henrike Illig
Isabel Marie Matthias